Built upon the traditional codecs, Pu et al.  [1]} apply
a task-specific metric into JPEG 2000. Liu et al.  [2]} enhance the compression scheme for intelligent applications by minimizing the distortion of frequency features that are important to neural network. CDVS [3]} and CDVA [3]} aim at efficiently supporting the search task through compact descriptors using both the traditional method and learning-based method. Li et al.  [5]} implement semantic-aware bit-allocation for the traditional codec based on reinforcement learning. On the other hand, based on the learning-based coding schemes, Chen et al.  [6]} propose a learning based facial image compression (LFIC) framework with a novel regionally adaptive pooling (RAP) module that can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric. Alvar et al.  [7]} study a bit allocation method for feature compression in a multi-task problem. These traditional hybrid video coding framework and the aforementioned learning-based methods both
encode the video into binary stream without any semantic
structure, which makes such bitstream failed to directly support intelligent tasks. Zhang et al. [8]} propose a hybrid content-plus-feature coding scheme framework of jointly compressing the feature descriptors and visual content. A novel rate-accuracy optimization technique is proposed to accurately estimate the retrieval performance degradation in feature coding. Duan et al.  [9]} carry out exploration in the new video coding for machines (VCM) area by building a bridge between feature coding for machine vision and video coding for human vision. They propose a task-specific compression pipeline that jointly trains the feature compression and intelligent tasks. Xin Li et al.  [5]} implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL) via designing semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Ma et al.  [11]} provide a systematical overview and analysis on the joint feature and texture representation
framework, which aims to smartly and coherently represent the visual information with the front-end intelligence in the scenario of video big data applications. Besides,
the future joint coding scheme by incorporating the deep learning
features is envisioned, and future challenges toward seamless and
unified joint compression are discussed.
